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This paper provides a large deviation principle for Non-Markovian, Brownian motion driven stochastic differential equations with random coefficients. Similar to Gao and Liu \cite{GL}, this extends the corresponding results collected in…

Probability · Mathematics 2014-07-22 Jin Ma , Zhenjie Ren , Nizar Touzi , Jianfeng Zhang

We approximate stochastic processes in finite dimension by dynamical systems. We provide trajectorial estimates which are uniform with respect to the initial condition for a well chosen distance. This relies on some non-expansivity property…

Probability · Mathematics 2017-01-11 Vincent Bansaye

In this paper, we obtain almost sure invariance principles with rate of order $n^{1/p}\log^\beta n$, $2< p\le 4$, for sums associated to a sequence of reverse martingale differences. Then, we apply those results to obtain similar…

Probability · Mathematics 2012-09-18 Christophe Cuny , Florence Merlevede

We exhibit conditions under which the flow of marginal distributions of a discontinuous semimartingale $\xi$ can be matched by a Markov process, whose infinitesimal generator is expressed in terms of the local characteristics of $\xi$. Our…

Probability · Mathematics 2012-05-17 Amel Bentata , Rama Cont

In this paper we derive the moderate deviation principle for stationary sequences of bounded random variables under martingale-type conditions. Applications to functions of $\phi$-mixing sequences, contracting Markov chains, expanding maps…

Probability · Mathematics 2007-11-27 Jérôme Dedecker , Florence Merlevède , Magda Peligrad , Sergey Utev

In this work, we investigate the McKean-Vlasov stochastic partial differential equations driven by Poisson random measure. By adapting the variational framework, we prove the well-posedness and large deviation principle for a class of…

Probability · Mathematics 2025-08-05 Yuhang Jiang , Jinming Li , Shihu Li

For a fractional Brownian motion $B^H$ with Hurst parameter $H\in]{1/4},{1/2}[\cup]{1/2},1[$, multiple indefinite integrals on a simplex are constructed and the regularity of their sample paths are studied. Then, it is proved that the…

Probability · Mathematics 2007-05-23 Marta Sanz-Solé , Iván Torrecilla-Tarantino

We consider the following quasi-linear parabolic system of backward partial differential equations on a Banach space $E$: $(\partial_t+L)u+f(\cdot,\cdot,u, A^{1/2}\nabla u)=0$ on $[0,T]\times E,\qquad u_T=\phi$, where $L$ is a possibly…

Probability · Mathematics 2012-01-17 Rongchan Zhu

We prove the large deviation principle for the law of the solutions to a class of parabolic semilinear stochastic partial differential equations driven by multiplicative noise, in $C\big([0,T]:L^\rho(D)\big)$, where $D\subset {\mathbb R}^d$…

Probability · Mathematics 2020-10-28 Leila Setayeshgar

The main results in this paper concern large deviations for families of non-Gaussian processes obtained as suitable perturbations of continuous centered multivariate Gaussian processes which satisfy a large deviation principle. We present…

Probability · Mathematics 2023-07-06 C. Macci , B. Pacchiarotti

The Whittaker 2d growth model is a triangular continuous Markov diffusion process that appears in many scientific contexts. It has been theoretically intriguing to establish a large deviation principle for this 2d process with a scaling…

Probability · Mathematics 2020-09-29 Jun Gao , Jie Ding

In this paper, we consider a class of reflected stochastic differential equations for which the constraint is not on the paths of the solution but on its law. We establish a small noise large deviation principle, a large deviation for short…

Probability · Mathematics 2023-03-27 Ping Chen , Jianliang Zhai

A peculiar feature of It\^o's calculus is that it is an integral calculus that gives no explicit derivative with a systematic differentiation theory counterpart, as in elementary calculus. So, can we define a pathwise stochastic derivative…

Probability · Mathematics 2010-05-25 Hassan Allouba

We show a deviation inequality inequalities for multi-indexed martingale We then provide applications to kernel regression for random fields and rates in the law of large numbers for orthomartingale difference random fields.

Probability · Mathematics 2024-08-27 Davide Giraudo

In this paper, we study small noise asymptotics of Markov-modulated diffusion processes in the regime that the modulating Markov chain is rapidly switching. We prove the joint sample-path large deviations principle for the Markov-modulated…

Probability · Mathematics 2023-02-27 Gang Huang , Michel Mandjes , Peter Spreij

We study large deviations from the invariant measure for nonlinear Schr\"odinger equations with colored noises on determining modes. The proof is based on a new abstract criterion, inspired by [V. Jak\v{s}i\'{c} et al., Comm. Pure Appl.…

Analysis of PDEs · Mathematics 2026-02-03 Yuxuan Chen , Shengquan Xiang

We establish a large deviation principle for time dependent trajectories (paths) of the empirical density of $N$ particles with long range interactions, for homogeneous systems. This result extends the classical kinetic theory that leads to…

Statistical Mechanics · Physics 2022-01-19 Ouassim Feliachi , Freddy Bouchet

It is shown that under a certain condition on a semimartingale and a time-change, any stochastic integral driven by the time-changed semimartingale is a time-changed stochastic integral driven by the original semimartingale. As a direct…

Probability · Mathematics 2010-10-26 Kei Kobayashi

Birth-death processes form a natural class where ideas and results on large deviations can be tested. In this paper, we derive a large deviation principle under the assumption that the rate of a jump down (death) is growing asymptotically…

Probability · Mathematics 2023-08-21 N. D. Vvedenskaya , A. V. Logachov , Y. M. Suhov , A. A. Yambartsev

Large and moderate deviation probabilities play an important role in many applied areas, such as insurance and risk analysis. This paper studies the exact moderate and large deviation asymptotics in non-logarithmic form for linear processes…

Statistics Theory · Mathematics 2013-05-07 Magda Peligrad , Hailin Sang , Yunda Zhong , Wei Biao Wu
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